Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations390
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.6 KiB
Average record size in memory80.3 B

Variable types

Numeric10

Alerts

BMAD is highly overall correlated with BMAN and 7 other fieldsHigh correlation
BMAN is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BMAN2 is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BPAD is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BPAN is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BPAN2 is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BWAD is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BWAN is highly overall correlated with BMAD and 7 other fieldsHigh correlation
BWAN2 is highly overall correlated with BMAD and 7 other fieldsHigh correlation
CT has unique valuesUnique

Reproduction

Analysis started2024-10-15 18:00:30.520963
Analysis finished2024-10-15 18:00:59.361608
Duration28.84 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

CT
Real number (ℝ)

UNIQUE 

Distinct390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395424.71
Minimum10300
Maximum980700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:00:59.575827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10300
5-th percentile75085
Q1281425
median456552
Q3496800
95-th percentile562355
Maximum980700
Range970400
Interquartile range (IQR)215375

Descriptive statistics

Standard deviation164334.66
Coefficient of variation (CV)0.41559026
Kurtosis0.51334377
Mean395424.71
Median Absolute Deviation (MAD)55400
Skewness-0.59681596
Sum1.5421564 × 108
Variance2.7005879 × 1010
MonotonicityNot monotonic
2024-10-15T18:00:59.892717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100500 1
 
0.3%
484500 1
 
0.3%
492900 1
 
0.3%
492800 1
 
0.3%
492700 1
 
0.3%
491200 1
 
0.3%
491101 1
 
0.3%
490004 1
 
0.3%
490003 1
 
0.3%
490002 1
 
0.3%
Other values (380) 380
97.4%
ValueCountFrequency (%)
10300 1
0.3%
20100 1
0.3%
20300 1
0.3%
30500 1
0.3%
40200 1
0.3%
40500 1
0.3%
40600 1
0.3%
40900 1
0.3%
50100 1
0.3%
50600 1
0.3%
ValueCountFrequency (%)
980700 1
0.3%
980100 1
0.3%
980000 1
0.3%
564500 1
0.3%
564400 1
0.3%
564200 1
0.3%
564100 1
0.3%
564000 1
0.3%
563900 1
0.3%
563800 1
0.3%

BPAD
Real number (ℝ)

HIGH CORRELATION 

Distinct365
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.9923
Minimum4
Maximum6494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:00.271882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile555.8
Q1960.25
median1470.5
Q32253.25
95-th percentile3233.65
Maximum6494
Range6490
Interquartile range (IQR)1293

Descriptive statistics

Standard deviation902.1361
Coefficient of variation (CV)0.54378558
Kurtosis1.6941458
Mean1658.9923
Median Absolute Deviation (MAD)591
Skewness0.97773239
Sum647007
Variance813849.54
MonotonicityNot monotonic
2024-10-15T18:01:00.557864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1567 3
 
0.8%
732 2
 
0.5%
604 2
 
0.5%
2509 2
 
0.5%
1819 2
 
0.5%
973 2
 
0.5%
1495 2
 
0.5%
1821 2
 
0.5%
688 2
 
0.5%
707 2
 
0.5%
Other values (355) 369
94.6%
ValueCountFrequency (%)
4 1
0.3%
8 1
0.3%
10 1
0.3%
170 1
0.3%
204 1
0.3%
248 1
0.3%
330 1
0.3%
396 1
0.3%
401 1
0.3%
427 1
0.3%
ValueCountFrequency (%)
6494 1
0.3%
4652 1
0.3%
4560 1
0.3%
4165 1
0.3%
4081 1
0.3%
3988 1
0.3%
3742 1
0.3%
3695 1
0.3%
3645 1
0.3%
3582 1
0.3%

BPAN
Real number (ℝ)

HIGH CORRELATION 

Distinct213
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.24359
Minimum0
Maximum458
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:00.850805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.9
Q187
median137
Q3197
95-th percentile301.4
Maximum458
Range458
Interquartile range (IQR)110

Descriptive statistics

Standard deviation80.93175
Coefficient of variation (CV)0.54593761
Kurtosis0.56058569
Mean148.24359
Median Absolute Deviation (MAD)57
Skewness0.75986022
Sum57815
Variance6549.9482
MonotonicityNot monotonic
2024-10-15T18:01:01.140883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 6
 
1.5%
171 6
 
1.5%
150 6
 
1.5%
79 6
 
1.5%
88 5
 
1.3%
80 4
 
1.0%
91 4
 
1.0%
221 4
 
1.0%
187 4
 
1.0%
100 4
 
1.0%
Other values (203) 341
87.4%
ValueCountFrequency (%)
0 2
0.5%
1 1
0.3%
16 1
0.3%
19 1
0.3%
20 1
0.3%
23 1
0.3%
24 2
0.5%
27 1
0.3%
28 2
0.5%
30 1
0.3%
ValueCountFrequency (%)
458 1
0.3%
456 1
0.3%
379 1
0.3%
378 1
0.3%
366 1
0.3%
355 1
0.3%
352 1
0.3%
351 1
0.3%
347 1
0.3%
336 1
0.3%

BPAN2
Real number (ℝ)

HIGH CORRELATION 

Distinct174
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.82564
Minimum0
Maximum315
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:01.459953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26
Q163
median98.5
Q3144.75
95-th percentile197.55
Maximum315
Range315
Interquartile range (IQR)81.75

Descriptive statistics

Standard deviation56.031531
Coefficient of variation (CV)0.52947028
Kurtosis0.30352749
Mean105.82564
Median Absolute Deviation (MAD)38
Skewness0.62364638
Sum41272
Variance3139.5325
MonotonicityNot monotonic
2024-10-15T18:01:01.752737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 9
 
2.3%
58 7
 
1.8%
72 6
 
1.5%
95 6
 
1.5%
125 6
 
1.5%
53 5
 
1.3%
97 5
 
1.3%
108 5
 
1.3%
158 5
 
1.3%
85 5
 
1.3%
Other values (164) 331
84.9%
ValueCountFrequency (%)
0 2
0.5%
1 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
14 4
1.0%
15 1
 
0.3%
16 1
 
0.3%
19 1
 
0.3%
20 3
0.8%
22 2
0.5%
ValueCountFrequency (%)
315 1
0.3%
314 1
0.3%
266 1
0.3%
265 1
0.3%
259 1
0.3%
243 1
0.3%
242 1
0.3%
236 1
0.3%
232 1
0.3%
227 1
0.3%

BWAD
Real number (ℝ)

HIGH CORRELATION 

Distinct353
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean903.47179
Minimum3
Maximum3294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:02.050172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile300.8
Q1537.25
median803
Q31219.75
95-th percentile1726.65
Maximum3294
Range3291
Interquartile range (IQR)682.5

Descriptive statistics

Standard deviation476.73317
Coefficient of variation (CV)0.52766802
Kurtosis1.0957319
Mean903.47179
Median Absolute Deviation (MAD)323.5
Skewness0.84074821
Sum352354
Variance227274.52
MonotonicityNot monotonic
2024-10-15T18:01:02.365958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
589 3
 
0.8%
583 3
 
0.8%
645 2
 
0.5%
547 2
 
0.5%
948 2
 
0.5%
719 2
 
0.5%
1136 2
 
0.5%
521 2
 
0.5%
669 2
 
0.5%
1312 2
 
0.5%
Other values (343) 368
94.4%
ValueCountFrequency (%)
3 1
0.3%
5 1
0.3%
7 1
0.3%
95 1
0.3%
113 1
0.3%
138 1
0.3%
194 2
0.5%
213 1
0.3%
262 1
0.3%
267 1
0.3%
ValueCountFrequency (%)
3294 1
0.3%
2493 1
0.3%
2356 1
0.3%
2189 1
0.3%
2149 1
0.3%
2092 1
0.3%
1983 1
0.3%
1929 1
0.3%
1927 1
0.3%
1878 1
0.3%

BWAN
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.802564
Minimum0
Maximum246
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:02.679554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.9
Q146
median68.5
Q3100
95-th percentile147.1
Maximum246
Range246
Interquartile range (IQR)54

Descriptive statistics

Standard deviation41.631255
Coefficient of variation (CV)0.54920643
Kurtosis0.74954543
Mean75.802564
Median Absolute Deviation (MAD)25.5
Skewness0.81206984
Sum29563
Variance1733.1614
MonotonicityNot monotonic
2024-10-15T18:01:02.988098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 9
 
2.3%
65 9
 
2.3%
46 7
 
1.8%
48 7
 
1.8%
33 6
 
1.5%
92 6
 
1.5%
66 6
 
1.5%
68 6
 
1.5%
49 6
 
1.5%
97 6
 
1.5%
Other values (133) 322
82.6%
ValueCountFrequency (%)
0 2
0.5%
1 1
 
0.3%
6 1
 
0.3%
7 1
 
0.3%
9 1
 
0.3%
12 2
0.5%
13 3
0.8%
14 3
0.8%
15 2
0.5%
16 2
0.5%
ValueCountFrequency (%)
246 1
 
0.3%
218 1
 
0.3%
201 1
 
0.3%
199 1
 
0.3%
191 1
 
0.3%
190 1
 
0.3%
181 3
0.8%
180 2
0.5%
176 1
 
0.3%
173 1
 
0.3%

BWAN2
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.566667
Minimum0
Maximum169
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:03.316331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.45
Q133.25
median50.5
Q373.75
95-th percentile106
Maximum169
Range169
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation29.008543
Coefficient of variation (CV)0.53161654
Kurtosis0.37177171
Mean54.566667
Median Absolute Deviation (MAD)19
Skewness0.64051313
Sum21281
Variance841.49554
MonotonicityNot monotonic
2024-10-15T18:01:03.788332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 9
 
2.3%
44 9
 
2.3%
38 9
 
2.3%
53 9
 
2.3%
74 8
 
2.1%
79 8
 
2.1%
46 8
 
2.1%
37 8
 
2.1%
26 8
 
2.1%
54 8
 
2.1%
Other values (103) 306
78.5%
ValueCountFrequency (%)
0 2
0.5%
1 2
0.5%
4 1
 
0.3%
5 2
0.5%
6 1
 
0.3%
7 2
0.5%
8 2
0.5%
9 2
0.5%
10 1
 
0.3%
11 3
0.8%
ValueCountFrequency (%)
169 1
 
0.3%
148 1
 
0.3%
144 1
 
0.3%
137 1
 
0.3%
134 1
 
0.3%
130 1
 
0.3%
128 1
 
0.3%
126 1
 
0.3%
115 2
0.5%
112 3
0.8%

BMAD
Real number (ℝ)

HIGH CORRELATION 

Distinct336
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean755.52051
Minimum1
Maximum3200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:04.114773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile257.8
Q1437
median650.5
Q31019
95-th percentile1517.5
Maximum3200
Range3199
Interquartile range (IQR)582

Descriptive statistics

Standard deviation429.06603
Coefficient of variation (CV)0.56790784
Kurtosis2.4595892
Mean755.52051
Median Absolute Deviation (MAD)270.5
Skewness1.1432819
Sum294653
Variance184097.65
MonotonicityNot monotonic
2024-10-15T18:01:04.477978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680 4
 
1.0%
437 3
 
0.8%
261 3
 
0.8%
832 3
 
0.8%
443 2
 
0.5%
370 2
 
0.5%
1072 2
 
0.5%
519 2
 
0.5%
372 2
 
0.5%
496 2
 
0.5%
Other values (326) 365
93.6%
ValueCountFrequency (%)
1 2
0.5%
5 1
0.3%
57 1
0.3%
109 1
0.3%
110 1
0.3%
136 1
0.3%
155 1
0.3%
180 1
0.3%
183 1
0.3%
188 1
0.3%
ValueCountFrequency (%)
3200 1
0.3%
2204 1
0.3%
2159 1
0.3%
1978 1
0.3%
1976 1
0.3%
1932 1
0.3%
1896 1
0.3%
1768 1
0.3%
1721 1
0.3%
1662 1
0.3%

BMAN
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.441026
Minimum0
Maximum238
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:05.237840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.45
Q141
median66
Q399
95-th percentile152.55
Maximum238
Range238
Interquartile range (IQR)58

Descriptive statistics

Standard deviation41.853218
Coefficient of variation (CV)0.57775574
Kurtosis0.42016307
Mean72.441026
Median Absolute Deviation (MAD)28
Skewness0.78266632
Sum28252
Variance1751.6919
MonotonicityNot monotonic
2024-10-15T18:01:05.773925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 11
 
2.8%
52 8
 
2.1%
82 7
 
1.8%
27 7
 
1.8%
55 7
 
1.8%
81 7
 
1.8%
33 7
 
1.8%
106 6
 
1.5%
38 6
 
1.5%
73 6
 
1.5%
Other values (136) 318
81.5%
ValueCountFrequency (%)
0 3
0.8%
2 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
11 1
 
0.3%
12 1
 
0.3%
13 3
0.8%
14 2
 
0.5%
15 1
 
0.3%
16 6
1.5%
ValueCountFrequency (%)
238 1
0.3%
212 1
0.3%
197 1
0.3%
188 1
0.3%
186 1
0.3%
179 2
0.5%
178 1
0.3%
167 1
0.3%
166 1
0.3%
165 2
0.5%

BMAN2
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.258974
Minimum0
Maximum166
Zeros3
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2024-10-15T18:01:06.193935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q129
median48
Q369
95-th percentile102
Maximum166
Range166
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.04198
Coefficient of variation (CV)0.56657357
Kurtosis0.30250515
Mean51.258974
Median Absolute Deviation (MAD)20
Skewness0.69785584
Sum19991
Variance843.43662
MonotonicityNot monotonic
2024-10-15T18:01:06.690107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 9
 
2.3%
23 9
 
2.3%
52 9
 
2.3%
44 8
 
2.1%
31 8
 
2.1%
32 8
 
2.1%
76 7
 
1.8%
59 7
 
1.8%
54 7
 
1.8%
48 7
 
1.8%
Other values (101) 311
79.7%
ValueCountFrequency (%)
0 3
0.8%
2 1
 
0.3%
7 4
1.0%
9 3
0.8%
10 4
1.0%
11 7
1.8%
12 2
 
0.5%
13 4
1.0%
14 4
1.0%
15 1
 
0.3%
ValueCountFrequency (%)
166 1
0.3%
146 1
0.3%
137 1
0.3%
129 1
0.3%
127 1
0.3%
125 2
0.5%
121 1
0.3%
119 1
0.3%
118 1
0.3%
117 1
0.3%

Interactions

2024-10-15T18:00:56.182269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:30.923194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:33.501102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:35.901726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:38.985457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:42.275468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:44.732811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:47.350320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:49.831017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:52.670589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:56.416936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:31.164710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:33.727433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:36.161007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:39.364139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:42.546278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:44.982955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:47.613695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:50.084987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:52.946080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:56.672368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:31.444113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:33.958746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:36.418822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:39.738262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:42.790321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:45.244169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:47.858252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:50.336803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:53.309998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:56.890712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:31.822690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:34.215298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:36.631764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:40.099669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:43.020076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:45.456930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:48.102415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:50.569280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:53.626673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:57.185378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:32.075087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:34.453562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:36.867172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:40.432441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:43.278945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:45.704403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:48.355052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:50.812998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:53.972168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:57.434072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:32.311896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:34.698381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:37.106839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:40.808892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:43.536071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:46.168011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:48.611521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:51.062516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:54.279335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:57.670929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:32.541256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:34.929256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:37.537245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:41.204801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:43.759095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:46.404443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:48.854010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:51.303261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:54.581376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:57.917334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:32.779696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:35.187996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:37.873849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:41.577364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:44.010686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:46.655590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:49.108934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:51.589530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:54.935168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:58.201560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:33.031855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:35.445169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:38.263042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:41.820736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:44.261616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:46.882126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:49.362581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:51.966077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:55.334388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:58.427366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:33.254754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:35.667425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:38.608760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:42.035628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:44.498616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:47.112573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:49.587372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:52.337778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-15T18:00:55.912236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-15T18:01:07.007533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BMADBMANBMAN2BPADBPANBPAN2BWADBWANBWAN2CT
BMAD1.0000.8970.8980.9950.8450.8470.9840.7450.7400.146
BMAN0.8971.0000.9870.9060.9680.9590.9060.8840.8710.275
BMAN20.8980.9871.0000.9080.9580.9660.9080.8760.8700.257
BPAD0.9950.9060.9081.0000.8700.8720.9960.7830.7780.157
BPAN0.8450.9680.9580.8701.0000.9900.8840.9710.9560.278
BPAN20.8470.9590.9660.8720.9901.0000.8850.9620.9670.257
BWAD0.9840.9060.9080.9960.8840.8851.0000.8080.8040.167
BWAN0.7450.8840.8760.7830.9710.9620.8081.0000.9840.273
BWAN20.7400.8710.8700.7780.9560.9670.8040.9841.0000.252
CT0.1460.2750.2570.1570.2780.2570.1670.2730.2521.000

Missing values

2024-10-15T18:00:58.762657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-15T18:00:59.178446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CTBPADBPANBPAN2BWADBWANBWAN2BMADBMANBMAN2
0100500123717913071297725258258
11011001080976558348334974932
21014001865157118103376578328161
310160069866443974835301189
4101700928876555554443733321
5101800147714511182781646506447
610300674503629925203752516
7110200193012185105453378766848
81106001477796181343356643626
911130012211157270467415174831
CTBPADBPANBPAN2BWADBWANBWAN2BMADBMANBMAN2
3808040056936222912092781613
38180600893382645822154351611
3828070060328143251242781610
38380900818766344843373703326
38490100917795848045314373427
3859020012561218971558455416344
38690300778846742641353524332
387980000411311100
388980100800700100
3899807001000500500